Upgraded AHS Wellbeing Through the Joining of Vehicle Control and Correspondence

1839 days ago, 625 views
PowerPoint PPT Presentation
Vehicle Dynamics Lab. Research Areas. Tire/street grating estimation ... expect a static model for vehicle 3% blunder contrasting with element vehicle model ...

Presentation Transcript

Slide 1

Improved AHS Safety Through the Integration of Vehicle Control and Communication October 2002 J. K. Hedrick, R. Sengupta, Q. Xu, C. Lee, Y. Kang

Slide 2

Communication engineering Goal = remote system valuable for: 1) Cooperative grinding estimation 2) Cooperative Emergency Maneuver 3) AHS and agreeable versatile journey control Emergency Braking Maneuvers Tire/street rubbing estimation Goal: Develop a sheltered control technique for the crisis braking move of detachments. Objective: Real time estimation of greatest tire-street grinding Research Areas

Slide 3

Communication design Tire/street rubbing estimation Road Condition & Maximum Friction Coeff. Data from different vehicles and street side infra structure. Crisis Braking Maneuvers " SAFETY " Project Goals

Slide 4

Slip-based Road Condition Estimation

Slide 5

Overview and Benefits of the Research Overview Empirical approach Effect-based technique Real Time work Benefits Estimation of max. increasing speed point of confinement of the vehicle. Crisis Braking Control for the Platoon. Street Condition versus Position Map.

Slide 6

Slip/Friction Coefficient Calculation Friction Coefficient Slip Max Acceleration Maximum erosion coefficients decides most extreme speeding up or deceleration

Slide 7

Slip Slope Vs ��  Affecting Factors Road Condition Tire Type Tread Pattern Tread Depth Velocity ��  Focus on Linear Region �� Slip Slope, k

Slide 8

Schematic of the Estimator

Slide 9

Static Normal Force Observer Normal Force �� friction coefficient figuring �� effective tire span estimation Static typical constrain spectator expect a static model for vehicle�� 3% blunder contrasting with element vehicle show Static Normal Force Model

Slide 10

Effective Tire Radius Observer Tire range is required for Slip Calculation Tire Radius Change is a Function of Normal Force Tire Pressure Velocity

Slide 11

Tractive Force Estimation

Slide 12

Brake Gain Estimator Brake Gain Change Heat Water Wear of Brake Pad, and so on �� sometimes changes more than half Model : Front Wheel Dynamics Method : Recursive Least Square Method Using Bounded Forgetting

Slide 13

Slip Slope Vs Each vehicle has its own slip slant under same street Dry street condition �� set as Reference slip slant Maximum rubbing coefficient change rate/Slip incline change rate in view of reference slip slant �� Linear Assumption

Slide 14

Slip Slope and Estimation Example : Wet ��  Dry Slip Slope Estimation Using RLS technique Estimation Based on slip slant

Slide 15

Emergency Braking Maneuvers

Slide 16

Emergency Braking Controller (Longitudinal Control) Requirements for crisis braking Control power against the high slip condition amongst street and tire. Erosion coefficients estimation calculation. Control technique : Dynamic Surface Control

Slide 17

Experimental Set-up Experimental Vehicle utilized : Ford red Lincoln town auto Sensors and actuators : wheel speed sensors brake weight sensors 5 th wheel speed sensor brake and throttle actuators Computers with continuous OS : QNX working framework

Slide 18

Simulation Results Simulation with Dynamic Surface Control (6 m/s 2 deceleration)

Slide 19

Experimental Results Experiments in same circumstance ( 6 m/s 2 deceleration)

Slide 20

Analysis of the Result The execution of the Dynamic Surface Controller is sensible (1~2m blunder with –6 m/s 2 ) in a crisis braking circumstance. Control execution relies on upon erosion coefficients.  "Erosion coefficient estimation" is important.

Slide 21

( Emergency Braking Control Strategies for Platoons Emergency braking for the Platoons : Difference of max. deceleration constrain between vehicles can bring about impact. Agreeable Emergency Control. ) Vehicle with Worst Braking Capacity transmits its data.

Slide 22

Simulation Results Space & speed following [m/s] [m]